Lab 11

Author

Carissa Feliciano

I. Reading and processing the New York Times (NYT) state-level COVID-19 data

1. Read in the data

# Load COVID state-level data from NYT
cv_states <- as.data.frame(data.table::fread("https://raw.githubusercontent.com/nytimes/covid-19-data/master/us-states.csv"))

# Load state population data 
state_pops <- as.data.frame(data.table::fread("https://raw.githubusercontent.com/COVID19Tracking/associated-data/master/us_census_data/us_census_2018_population_estimates_states.csv"))
state_pops$abb <- state_pops$state
state_pops$state <- state_pops$state_name
state_pops$state_name <- NULL

# Merge
cv_states <- merge(cv_states, state_pops, by="state")

2. Look at the data

# Inspect dimensions, head, and tail of the data
dim(cv_states)
[1] 58094     9
head(cv_states)
    state       date fips   cases deaths geo_id population pop_density abb
1 Alabama 2023-01-04    1 1587224  21263      1    4887871    96.50939  AL
2 Alabama 2020-04-25    1    6213    213      1    4887871    96.50939  AL
3 Alabama 2023-02-26    1 1638348  21400      1    4887871    96.50939  AL
4 Alabama 2022-12-03    1 1549285  21129      1    4887871    96.50939  AL
5 Alabama 2020-05-06    1    8691    343      1    4887871    96.50939  AL
6 Alabama 2021-04-21    1  524367  10807      1    4887871    96.50939  AL
tail(cv_states)
        state       date fips  cases deaths geo_id population pop_density abb
58089 Wyoming 2022-09-11   56 175290   1884     56     577737    5.950611  WY
58090 Wyoming 2022-08-21   56 173487   1871     56     577737    5.950611  WY
58091 Wyoming 2021-01-26   56  51152    596     56     577737    5.950611  WY
58092 Wyoming 2021-02-21   56  53795    662     56     577737    5.950611  WY
58093 Wyoming 2021-08-22   56  70671    809     56     577737    5.950611  WY
58094 Wyoming 2021-03-20   56  55581    693     56     577737    5.950611  WY
# Inspect the structure of the variables
str(cv_states)
'data.frame':   58094 obs. of  9 variables:
 $ state      : chr  "Alabama" "Alabama" "Alabama" "Alabama" ...
 $ date       : IDate, format: "2023-01-04" "2020-04-25" ...
 $ fips       : int  1 1 1 1 1 1 1 1 1 1 ...
 $ cases      : int  1587224 6213 1638348 1549285 8691 524367 1321892 1088370 1153149 814025 ...
 $ deaths     : int  21263 213 21400 21129 343 10807 19676 16756 16826 15179 ...
 $ geo_id     : int  1 1 1 1 1 1 1 1 1 1 ...
 $ population : int  4887871 4887871 4887871 4887871 4887871 4887871 4887871 4887871 4887871 4887871 ...
 $ pop_density: num  96.5 96.5 96.5 96.5 96.5 ...
 $ abb        : chr  "AL" "AL" "AL" "AL" ...

The “state” and “abb” variables could be factors instead of characters.

3. Format the data

# Make the date into a date variable
cv_states$date <- as.Date(cv_states$date, format="%Y-%m-%d")

# Format the state and state abbreviation (abb) variables
state_list <- unique(cv_states$state)
cv_states$state <- factor(cv_states$state, levels = state_list)
abb_list <- unique(cv_states$abb)
cv_states$abb <- factor(cv_states$abb, levels = abb_list)

# Order the data first by state, second by date
cv_states = cv_states[order(cv_states$state, cv_states$date),]

# Confirm the variables are now correctly formatted
str(cv_states)
'data.frame':   58094 obs. of  9 variables:
 $ state      : Factor w/ 52 levels "Alabama","Alaska",..: 1 1 1 1 1 1 1 1 1 1 ...
 $ date       : Date, format: "2020-03-13" "2020-03-14" ...
 $ fips       : int  1 1 1 1 1 1 1 1 1 1 ...
 $ cases      : int  6 12 23 29 39 51 78 106 131 157 ...
 $ deaths     : int  0 0 0 0 0 0 0 0 0 0 ...
 $ geo_id     : int  1 1 1 1 1 1 1 1 1 1 ...
 $ population : int  4887871 4887871 4887871 4887871 4887871 4887871 4887871 4887871 4887871 4887871 ...
 $ pop_density: num  96.5 96.5 96.5 96.5 96.5 ...
 $ abb        : Factor w/ 52 levels "AL","AK","AZ",..: 1 1 1 1 1 1 1 1 1 1 ...
head(cv_states)
       state       date fips cases deaths geo_id population pop_density abb
1029 Alabama 2020-03-13    1     6      0      1    4887871    96.50939  AL
597  Alabama 2020-03-14    1    12      0      1    4887871    96.50939  AL
282  Alabama 2020-03-15    1    23      0      1    4887871    96.50939  AL
12   Alabama 2020-03-16    1    29      0      1    4887871    96.50939  AL
266  Alabama 2020-03-17    1    39      0      1    4887871    96.50939  AL
78   Alabama 2020-03-18    1    51      0      1    4887871    96.50939  AL
tail(cv_states)
        state       date fips  cases deaths geo_id population pop_density abb
57902 Wyoming 2023-03-18   56 185640   2009     56     577737    5.950611  WY
57916 Wyoming 2023-03-19   56 185640   2009     56     577737    5.950611  WY
57647 Wyoming 2023-03-20   56 185640   2009     56     577737    5.950611  WY
57867 Wyoming 2023-03-21   56 185800   2014     56     577737    5.950611  WY
58057 Wyoming 2023-03-22   56 185800   2014     56     577737    5.950611  WY
57812 Wyoming 2023-03-23   56 185800   2014     56     577737    5.950611  WY
# Inspect the range values for each variable. What is the date range? The range of cases and deaths?
summary(cv_states)
           state            date                 fips           cases         
 Washington   : 1158   Min.   :2020-01-21   Min.   : 1.00   Min.   :       1  
 Illinois     : 1155   1st Qu.:2020-12-06   1st Qu.:16.00   1st Qu.:  112125  
 California   : 1154   Median :2021-09-11   Median :29.00   Median :  418120  
 Arizona      : 1153   Mean   :2021-09-10   Mean   :29.78   Mean   :  947941  
 Massachusetts: 1147   3rd Qu.:2022-06-17   3rd Qu.:44.00   3rd Qu.: 1134318  
 Wisconsin    : 1143   Max.   :2023-03-23   Max.   :72.00   Max.   :12169158  
 (Other)      :51184                                                          
     deaths           geo_id        population        pop_density       
 Min.   :     0   Min.   : 1.00   Min.   :  577737   Min.   :    1.292  
 1st Qu.:  1598   1st Qu.:16.00   1st Qu.: 1805832   1st Qu.:   43.659  
 Median :  5901   Median :29.00   Median : 4468402   Median :  107.860  
 Mean   : 12553   Mean   :29.78   Mean   : 6397965   Mean   :  423.031  
 3rd Qu.: 15952   3rd Qu.:44.00   3rd Qu.: 7535591   3rd Qu.:  229.511  
 Max.   :104277   Max.   :72.00   Max.   :39557045   Max.   :11490.120  
                                                     NA's   :1106       
      abb       
 WA     : 1158  
 IL     : 1155  
 CA     : 1154  
 AZ     : 1153  
 MA     : 1147  
 WI     : 1143  
 (Other):51184  
min(cv_states$date)
[1] "2020-01-21"
max(cv_states$date)
[1] "2023-03-23"

The date range is January 21, 2020 to March 23, 2023. The range of cases is 1 to 12169158. The range of deaths is 0 to 104277.

4. Add new_cases and new_deaths and correct outliers

# Add variables for new_cases and new_deaths:
for (i in 1:length(state_list)) {
  cv_subset = subset(cv_states, state == state_list[i])
  cv_subset = cv_subset[order(cv_subset$date),]

# Add starting level for new cases and deaths
  cv_subset$new_cases = cv_subset$cases[1]
  cv_subset$new_deaths = cv_subset$deaths[1]

# Calculate new cases and new deaths for each date
for (j in 2:nrow(cv_subset)) {
    cv_subset$new_cases[j] <- cv_subset$cases[j] - cv_subset$cases[j - 1]
    cv_subset$new_deaths[j] <- cv_subset$deaths[j] - cv_subset$deaths[j - 1]
}
  
# Include in main dataset
  cv_states$new_cases[cv_states$state==state_list[i]] = cv_subset$new_cases
  cv_states$new_deaths[cv_states$state==state_list[i]] = cv_subset$new_deaths
}

# Focus on recent dates
cv_states <- cv_states |> dplyr::filter(date >= "2021-06-01")
# Inspect outliers in new_cases using plotly
library(ggplot2)
library(plotly)

Attaching package: 'plotly'
The following object is masked from 'package:ggplot2':

    last_plot
The following object is masked from 'package:stats':

    filter
The following object is masked from 'package:graphics':

    layout
p1<-ggplot(cv_states, aes(x = date, y = new_cases, color = state)) + 
  geom_line() + 
  geom_point(size = .5, alpha = 0.5)
ggplotly(p1)
p1<-NULL
p2<-ggplot(cv_states, aes(x = date, y = new_deaths, color = state)) + 
  geom_line() + 
  geom_point(size = .5, alpha = 0.5)
ggplotly(p2)
p2<-NULL # to clear from workspace

New cases:
Strange values:
- On 2021-06-04, Florida has -40527 cases, which does not make sense.
- On 2022-01-29, Colorado has -4678 cases.
- On 2022-02-08, Pennsylvania has -4397 cases.
Possible outliers:
- On 2022-01-10, California had 227,972 new cases.
- On 2022-01-17, California had 221,235 new cases.

New deaths:
Strange values:
- On 2021-06-04, California had -375 new deaths
- On 2022-04–5, West Virginia has -123 new deaths.
Possible outliers:
- On 2022-11-11, New York had 3732 new deaths.
- On 2022-11-11, California had 2363 new deaths.

# Set negative new case or death counts to 0
cv_states$new_cases[cv_states$new_cases<0] = 0
cv_states$new_deaths[cv_states$new_deaths<0] = 0

# Recalculate `cases` and `deaths` as cumulative sum of updated `new_cases` and `new_deaths`
for (i in 1:length(state_list)) {
  cv_subset = subset(cv_states, state == state_list[i])

  # Add starting level for cases and death
  cv_subset$cases = cv_subset$new_cases[1]
  cv_subset$deaths = cv_subset$new_deaths[1]
  
    #Calculate cases and death as cumulative sum of new_cases and new_deaths 
  for (j in 2:nrow(cv_subset)) {
    cv_subset$cases[j] = cv_subset$new_cases[j] + cv_subset$cases[j-1]
    cv_subset$deaths[j] = cv_subset$new_deaths[j] + cv_subset$deaths[j-1]
  }
  # include in main dataset
  cv_states$cases[cv_states$state==state_list[i]] = cv_subset$cases
  cv_states$deaths[cv_states$state==state_list[i]] = cv_subset$deaths
}

# Smooth new counts
cv_states$new_cases = zoo::rollmean(cv_states$new_cases, k=7, fill=NA, align='right') %>% round(digits = 0)
cv_states$new_deaths = zoo::rollmean(cv_states$new_deaths, k=7, fill=NA, align='right') %>% round(digits = 0)
# Inspect data again interactively
p2<-ggplot(cv_states, aes(x = date, y = new_deaths, color = state)) + geom_line() + geom_point(size = .5, alpha = 0.5)
ggplotly(p2)
#p2=NULL

Some outliers:
On 2022-11-11, New York had 553 deaths
On 2021-12-28, Tennessee had 363 deaths
On 2021-11-24, Missouri had 340 deaths

5. Add additional variables

# Add population normalized (by 100,000) counts for each variable
cv_states$per100k =  as.numeric(format(round(cv_states$cases/(cv_states$population/100000),1),nsmall=1))
cv_states$newper100k =  as.numeric(format(round(cv_states$new_cases/(cv_states$population/100000),1),nsmall=1))
Warning: NAs introduced by coercion
cv_states$deathsper100k =  as.numeric(format(round(cv_states$deaths/(cv_states$population/100000),1),nsmall=1))
cv_states$newdeathsper100k =  as.numeric(format(round(cv_states$new_deaths/(cv_states$population/100000),1),nsmall=1))
Warning: NAs introduced by coercion
# add a naive_CFR variable = deaths / cases
cv_states = cv_states |> mutate(naive_CFR = round((deaths*100/cases),2))

# create a `cv_states_today` variable
cv_states_today = subset(cv_states, date==max(cv_states$date))

II. Scatterplots

6. Explore scatterplots using plot_ly()

Pop_density vs cases

# pop_density vs. cases
cv_states_today |>
  plot_ly(x = ~pop_density, y = ~cases, 
          type = 'scatter', 
          mode = 'markers', 
          color = ~state,
          size = ~population, sizes = c(5, 70), marker = list(sizemode='diameter', opacity=0.5))
Warning: Ignoring 1 observations
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Returning the palette you asked for with that many colors
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District of Columbia is an outlier.

# filter out "District of Columbia"
cv_states_today_filter <- cv_states_today |> filter(state!="District of Columbia")

# pop_density vs cases after filtering
cv_states_today_filter |>
  plot_ly(x = ~pop_density, y = ~cases, 
          type = 'scatter', 
          mode = 'markers', 
          color = ~state,
          size = ~population, sizes = c(5, 70), marker = list(sizemode='diameter', opacity=0.5))
Warning: Ignoring 1 observations
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Pop_density vs cases per 100k

# pop_density vs cases per 100k
cv_states_today |>
  plot_ly(x = ~pop_density, y = ~per100k, 
          type = 'scatter', 
          mode = 'markers', 
          color = ~state,
          size = ~population, sizes = c(5, 70), marker = list(sizemode='diameter', opacity=0.5))
Warning: Ignoring 1 observations
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District of Columbia is an outlier again.

# pop_density vs cases per 100k after filtering
cv_states_today_filter |>
  plot_ly(x = ~pop_density, y = ~per100k, 
          type = 'scatter', 
          mode = 'markers', 
          color = ~state,
          size = ~population, sizes = c(5, 70), marker = list(sizemode='diameter', opacity=0.5))
Warning: Ignoring 1 observations
Warning in RColorBrewer::brewer.pal(N, "Set2"): n too large, allowed maximum for palette Set2 is 8
Returning the palette you asked for with that many colors
Warning in RColorBrewer::brewer.pal(N, "Set2"): n too large, allowed maximum for palette Set2 is 8
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Pop_density vs deaths

#pop_density vs deaths
cv_states_today |>
  plot_ly(x = ~pop_density, y = ~deaths, 
          type = 'scatter', 
          mode = 'markers', 
          color = ~state,
          size = ~population, sizes = c(5, 70), marker = list(sizemode='diameter', opacity=0.5))
Warning: Ignoring 1 observations
Warning in RColorBrewer::brewer.pal(N, "Set2"): n too large, allowed maximum for palette Set2 is 8
Returning the palette you asked for with that many colors
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District of Columbia is an outlier again.

cv_states_today_filter |>
  plot_ly(x = ~pop_density, y = ~deaths, 
          type = 'scatter', 
          mode = 'markers', 
          color = ~state,
          size = ~population, sizes = c(5, 70), marker = list(sizemode='diameter', opacity=0.5))
Warning: Ignoring 1 observations
Warning in RColorBrewer::brewer.pal(N, "Set2"): n too large, allowed maximum for palette Set2 is 8
Returning the palette you asked for with that many colors
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Pop_density vs deaths per 100k

cv_states_today |>
  plot_ly(x = ~pop_density, y = ~deathsper100k, 
          type = 'scatter', 
          mode = 'markers', 
          color = ~state,
          size = ~population, sizes = c(5, 70), marker = list(sizemode='diameter', opacity=0.5))
Warning: Ignoring 1 observations
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District of columbia is an outlier again.

cv_states_today_filter |>
  plot_ly(x = ~pop_density, y = ~deathsper100k, 
          type = 'scatter', 
          mode = 'markers', 
          color = ~state,
          size = ~population, sizes = c(5, 70), marker = list(sizemode='diameter', opacity=0.5))
Warning: Ignoring 1 observations
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Choose one plot

cv_states_today_filter |>
  plot_ly(x = ~pop_density, y = ~per100k, 
          type = 'scatter', 
          mode = 'markers', 
          color = ~state,
          size = ~population, sizes = c(5, 70), marker = list(sizemode='diameter', opacity=0.5),
          hoverinfo = 'text',
          text = ~paste( paste(state, ":", sep=""),
                         paste(" Cases per 100k: ", per100k, sep=""),
                         paste(" Deaths per 100k: ", deathsper100k, sep=""),
                         sep = "<br>")) |>
  layout(title = "Population-normalized COVID-19 deaths (per 100k) vs. Population density for US States",
         yaxis = list(title = "Cases per 100k"),
         xaxis = list(title = "Population Density"),
         hovermode = "compare")
Warning: Ignoring 1 observations
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7. Explore scatterplot trend interactively using ggplotly() and geom_smooth()

p <- ggplot(cv_states_today_filter, aes(x=pop_density, y=newdeathsper100k, size=population)) + 
      geom_point() +
      geom_smooth()
ggplotly(p)
`geom_smooth()` using method = 'loess' and formula = 'y ~ x'
Warning: Removed 1 row containing non-finite outside the scale range
(`stat_smooth()`).
Warning: The following aesthetics were dropped during statistical transformation: size.
ℹ This can happen when ggplot fails to infer the correct grouping structure in
  the data.
ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
  variable into a factor?

Most of the states have a population density between 0 and 250. The slope of the line beyond that point is primarily dictated by a handful of states. When looking at the points between 0 and 250 pop_density, it appears that there is no relationship between pop_density and new deaths per 100k. Overall, I do not think population density and new deaths per 100k are correlated.

8. Multiple line chart

# Line chart for naive_CFR for all states over time using `plot_ly()`
plot_ly(cv_states, x = ~date, y = ~naive_CFR, color = ~state, type = "scatter", mode = "lines")
Warning in RColorBrewer::brewer.pal(N, "Set2"): n too large, allowed maximum for palette Set2 is 8
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The states that had an increase in CFR in September 2021 included Arkansas, Florida, Georgia, Hawaii, Idaho, Indiana, Kentucky, Louisiana, Mississippi, Nevada, Oklahoma, Oregon, Puerto Rico, South Carolina, Texas, and West Virginia. The CFR continued to increase until January 2022. It dipped in January 2022 and then slightly increased and plateaued in March 2022.

# Line chart for Florida showing new_cases and new_deaths together
cv_states |> filter(state=="Florida") |>
  plot_ly(x = ~date, y = ~new_cases, type = "scatter", mode = "lines", name = "New Cases") |>
  add_trace(x = ~date, y = ~new_deaths, type = "scatter", mode = "lines", name = "New Deaths") 

The approximate peak of deaths is 445 deaths on September 9, 2021. The approximate peak of cases is 84.699k cases on January 10, 2022. However, the approximate peak of cases corresponding to the peak of deaths is 29.711k cases on August 16, 2021. The time delay between the peak of cases and the peak of deaths is 24 days.

9. Heatmaps

# Map state, date, and new_cases to a matrix
library(tidyr)
cv_states_mat <- cv_states |> select(state, date, new_cases) |> dplyr::filter(date>as.Date("2021-06-15"))

cv_states_mat2 <- as.data.frame(pivot_wider(cv_states_mat, 
                                            names_from = state, 
                                            values_from = new_cases))
rownames(cv_states_mat2) <- cv_states_mat2$date
cv_states_mat2$date <- NULL
cv_states_mat2 <- as.matrix(cv_states_mat2)

# Create a heatmap using plot_ly()
plot_ly(x=colnames(cv_states_mat2), y=rownames(cv_states_mat2),
             z=~cv_states_mat2,
             type="heatmap",
             showscale=T)

California stands out.

# Repeat with newper100k
cv_states_mat <- cv_states |> select(state, date, newper100k) |> dplyr::filter(date>as.Date("2021-06-15"))
cv_states_mat2 <- as.data.frame(pivot_wider(cv_states_mat, 
                                            names_from = state, 
                                            values_from = newper100k))
rownames(cv_states_mat2) <- cv_states_mat2$date
cv_states_mat2$date <- NULL
cv_states_mat2 <- as.matrix(cv_states_mat2)

plot_ly(x=colnames(cv_states_mat2), y=rownames(cv_states_mat2),
             z=~cv_states_mat2,
             type="heatmap",
             showscale=T)

Now, Rhode Island sticks out.

# Create a second heatmap after filtering to only include dates every other week
filter_dates <- seq(as.Date("2021-06-15"), as.Date("2021-11-01"), by="2 weeks")

cv_states_mat <- cv_states |> select(state, date, new_cases) |> filter(date %in% filter_dates)
cv_states_mat2 <- as.data.frame(pivot_wider(cv_states_mat, 
                                            names_from = state, 
                                            values_from = new_cases))
rownames(cv_states_mat2) <- cv_states_mat2$date
cv_states_mat2$date <- NULL
cv_states_mat2 <- as.matrix(cv_states_mat2)

# Create a heatmap using plot_ly()
plot_ly(x=colnames(cv_states_mat2), y=rownames(cv_states_mat2),
             z=~cv_states_mat2,
             type="heatmap",
             showscale=T)

10. Map

### For specified date

pick.date = "2021-10-15"

# Extract the data for each state by its abbreviation
cv_per100 <- cv_states |> filter(date==pick.date) |> select(state, abb, newper100k, cases, deaths, naive_CFR) # select data
cv_per100$state_name <- cv_per100$state
cv_per100$state <- cv_per100$abb
cv_per100$abb <- NULL

# Create hover text
cv_per100$hover <- with(cv_per100, paste(state_name, '<br>',
                                         "Naive CFR: ", naive_CFR, '<br>',
                                         "Cases per 100k: ", newper100k, '<br>', 
                                         "Cases: ", cases, '<br>', 
                                         "Deaths: ", deaths))

# Set up mapping details
set_map_details <- list(
  scope = 'usa',
  projection = list(type = 'albers usa'),
  showlakes = TRUE,
  lakecolor = toRGB('white')
)

# Make sure both maps are on the same color scale
shadeLimit <- 2

# Create the map
fig <- plot_geo(cv_per100, locationmode = 'USA-states') |> 
  add_trace(
    z = ~naive_CFR, text = ~hover, locations = ~state,
    color = ~naive_CFR, colors = 'Purples'
  )
fig <- fig |> colorbar(title = paste0("Naive CFR: ", pick.date), limits = c(0,shadeLimit))
fig <- fig |> layout(
    title = paste('Naive Case Fatality Rates (CFRs) by State as of ', pick.date, '<br>(Hover for value)'),
    geo = set_map_details
  )
fig_pick.date <- fig


#############
### Map for today's date

# Extract the data for each state by its abbreviation
cv_per100 <- cv_states_today |>  select(state, abb, newper100k, cases, deaths, naive_CFR) # select data
cv_per100$state_name <- cv_per100$state
cv_per100$state <- cv_per100$abb
cv_per100$abb <- NULL

# Create hover text
cv_per100$hover <- with(cv_per100, paste(state_name, '<br>', 
                                         "Naive CFR: ", naive_CFR, '<br>',
                                         "Cases per 100k: ", newper100k, '<br>', 
                                         "Cases: ", cases, '<br>', 
                                         "Deaths: ", deaths))

# Set up mapping details
set_map_details <- list(
  scope = 'usa',
  projection = list(type = 'albers usa'),
  showlakes = TRUE,
  lakecolor = toRGB('white')
)

# Create the map
fig <- plot_geo(cv_per100, locationmode = 'USA-states') |> 
  add_trace(
    z = ~naive_CFR, text = ~hover, locations = ~state,
    color = ~naive_CFR, colors = 'Purples'
  )
fig <- fig |> colorbar(title = paste0("Naive CFR: ", Sys.Date()), limits = c(0,shadeLimit))
fig <- fig |> layout(
    title = paste('Naive Case Fatality Rates (CFRs) by State as of', Sys.Date(), '<br>(Hover for value)'),
    geo = set_map_details
  )
fig_Today <- fig


### Plot together 
subplot(fig_pick.date, fig_Today, nrows = 2, margin = .05)

On 2021-10-15, the CFRs were highest in Nevada (1.57) and Florida (1.57). Other states with high CFRs included Idaho, Texas, Arkansas, Mississippi, Alabama, and Georgia.

On 2024-11-12, Texas no longer has the highest CFR. The rest of the states generally followa similar pattern as in October 2021 in terms of relative CFRs. The Southeast region and Southwest have the higher CFRs. The Midwest and California have lower CFRs.